Description Usage Arguments Value Please cite as: Author(s) References See Also Examples
DynNom
is a generic function for displaying the results of an statistical model object as a dynamic nomogram in an 'RStudio' panel or web browser. DynNom
supports a variety of model objects; lm
, glm
, coxph
and also ols
, Glm
, lrm
, cph
models in the rms
package. It is a translational tool aiming to provide easy, informative individual predictions.
1 2 |
model |
an |
data |
dataframe containing the accompanying data |
clevel |
confidence level required |
m.summary |
The option to choose the format of the model output in the 'Summary Model' tab. If "raw" (the default) is chosen the result of summary(model) will be display while if "formatted" is chosen the model summary using the stargazer package will be displayed. |
covariate |
The option to choose the type of covariate(s) input control widget for numeric values. If "slider" (the default) is chosen a shiny application with slider control widgets are used while if "numeric" is chosen numeric values input controls will be displayed. |
ptype |
This plot type option relates to coxph objects only. If "st" (the default) is chosen, a plot of the estimated survivor function, S(t), is displayed. If "1-st" is chosen a plot of 1- S(t) is displayed. |
A dynamic nomogram in a shiny application which recognises all the predictors in the model and uses them to build a sidebar panel. It sets up drop down menus for factors and sliders set at the mean and bounded by the range for covariates.
The individual predictions with a relative confidence interval are calculated using the predict
function, displaying either graphically as an interactive plot in the Graphical Summary
tab or a table in the Numerical Summary
tab. A table of model output is also available in the Model Summary
tab. In the case of the Cox proportional hazards model, estimated survivor/death function will be additionally plotted in an extra tab.
Jalali, A., Roshan, D., Alvarez-Iglesias, A., Newell, J. (2017). Dynamic Nomograms for Linear, Generalized Linear and Proportional Hazard Models. R package version 4.1.
Amirhossein Jalali, Davood Roshan, Alberto Alvarez-Iglesias, John Newell
Maintainer: Amirhossein Jalali <a.jalali2@nuigalway.ie>
Banks, J. 2006. Nomograms. Encyclopedia of Statistical Sciences. 8.
Easy web applications in R. http://shiny.rstudio.com
Frank E Harrell Jr (2017). rms: Regression Modeling Strategies. R package version 4.5-0.
https://CRAN.R-project.org/package=rms
DynNom.lm
, DynNom.glm
, DynNom.coxph
, DynNom.ols
, DynNom.lrm
, DynNom.Glm
, DynNom.cph
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ## Not run:
# simple linear regression models
model1 <- lm(uptake ~ Plant + conc + Plant * conc, data = CO2)
DynNom(model1, CO2)
data1 <- data.frame(state.x77)
model2 <- ols(Life.Exp ~ Population + Income + Illiteracy + Murder + HS.Grad +
Frost + Area,data=data1)
DynNom(model2, data1)
# Generalized regression models
data2 =as.data.frame(Titanic)
model3 <- glm(Survived ~ Age + Class + Sex, data = data2, weights = Freq,
family = binomial("probit"))
DynNom(model3, data2, clevel = 0.9)
model4 <- lrm(formula= vs ~ wt + disp, data = mtcars)
DynNom(model4, mtcars, clevel = 0.9, m.summary = "formatted")
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
data2 = data.frame(counts, outcome, treatment)
model5 <- Glm((2 * counts) ~ outcome + treatment, family = poisson(), data = data2)
DynNom.Glm(model5, data2)
# a proportional hazard model
data.kidney <- kidney
# always make sure that the categorical variables are in a factor class
data.kidney$sex <- as.factor(data.kidney$sex)
levels(data.kidney$sex) <- c("male", "female")
model6 <- coxph(Surv(time, status) ~ age + sex + disease, data.kidney)
DynNom(model6, data.kidney)
DynNom(model6, data.kidney, ptype = "1-st")
model7 <-cph((Surv(log(time), status)) ~ rcs(age, 4) * strat(trt) +
diagtime * strat(prior) + lsp(karno, 60), data = veteran)
DynNom(model7, veteran)
## End(Not run)
if (interactive()) {
# a poisson regression model
model8 <- glm(event ~ mag + station + dist + accel, data = attenu, family = poisson)
DynNom(model8, attenu, covariate = "numeric")
}
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